Potential of Artificial Intelligence in Life Sciences (Clinical Trials and Pharmaceuticals)


 Ever imagined a situation where a system works on its own with the help of deep learning or machine learning capabilities, without being explicitly programmed. This type of technology that utilize data and machine learning is Artificial Intelligence; which is the most talked about technology for more than a decade.

This new wave of innovation is driving many industries and Life Science out of them is swaying with notable improvements. Intended to enhance human interactions, differentiating cells, improving medical imaging techniques and analysing genomic data; AI is well set up for life science applications. AI contribute to one of the major facets of biology; which is deep learning. If we analyse the structure of the brain, it is connected through various neural networks, each one analysing the data passed through it. Some or the other kind of data is extracted at each layer of neural network such as one neural layer analyse the cell membrane, the other layer works on organelle data and so on until the particular cell is amalgamated.

Medical/Clinical Research is the most compelling field requiring AI enabled technology. It will be no less to say that Clinical trials are an important aspect of developing new drugs and treatment for the life-taking medical conditions. Clinical trials are conducted in three stages that test more and more patients. But the traditional data techniques and higher patient drop out ratio make the process extremely time-consuming and complex. This negatively impacts not only clinics trying to examine new treatments but also the healthcare practitioners who prescribe particular treatment. This is where Artificial Intelligence get legacy systems to the new age of digital transformation.

Traditionally, research methodology only involved practitioners with subject knowledge or referring to search engines for facts. However, machine learning and AI platforms have changed the way research is being conducted. With intelligent machines, data analytics and inbuilt algorithms; healthcare firms have benefitted tremendously and for pharma/life sciences & clinical research firms, the opportunities are way more similar. Clinical research firms (CRO) are utilizing AI to manage regulatory information, compliances, clinical trials and research submissions. In this age of data overload, Artificial Intelligence is making clinical research faster, inexpensive and extremely targeted. AI allows research teams to focus on process automation, efficiencies, data insights, business decisions, precision and in drug designing & development. There are many issues and challenges faced by CROs and pharmaceutical organisations pertaining to data. Going forward in this article, we will discuss the issues, challenges, opportunities, and impact of AI in life science.


Data and Communication Challenges Faced by Pharma-

Clinical Research Organisations Life Science vertical gather data from multiple sources for various different purposes. There are numerous data analytics platforms and applications available in the market despite this only a few pharmaceutical companies are able to leverage data for their advantage. Here we enlist three major challenges: Discrepancy of Data Sources In order to be able to run a feasible analytics process, it is extremely essential to have access to all the data collected. However, the case is entirely different. For pharmaceutical and clinical research firm, fetching data is very difficult due to discrepancies in data. Usually, the data is stored in silos, which is accessed via different software platforms and applications with different data structures and models. Hence, if at any given point anyone wishes to access data, discrepancies need to be sorted to gain access to all levels/ layers of data.


Uncertainty in Data Accuracy-

The most critical of all the challenges is ambiguity around data accuracy. Most of the organizations used data analytics processes but have found uncertainty around the accuracy and relevance of data with respect to time. Since the data for pharma & clinical research firms are collected from silos and other dissimilar sources, it is no less than a challenge to understand the freshness of data. Not having enough accuracy of data is an issue with the research firms. On-Time Delivery of Analytics When data is collected from many different sources, it is difficult to synchronize all the data and run analytics process on the collected data set. This delays the timely delivery of insights from analytics thereby delaying business implications. Many organisations who do not have a data analytics system in place usually outsource data analysis to a third party or manually collate analytics report & data insights; consuming a lot of time.

Confined Regulations-

Global regulatory bodies such as EMA and FDA has set strict guidelines to conduct clinical trials. For any drug or treatment to be sanctioned in the market and ensuring patient safety; it is essential to be compliant to the specified regulations which make the process more complex and expensive. Making any drug or treatment compliant to regulations is yet another challenge for pharmaceutical firms, not because of the regulatory standards but because of the complex process which needs to follow for attaining approval.



Ensuring patient safety is the key concern for any healthcare establishment, whether clinical research, pharmaceutical, pathology labs or any other. An increase in the number of patients leads to an increase in the clinical data associated with the individual patient. Hence running analytics on this disparate data to gain insights from a collective set and making the decision based on the same becomes a complex process.



Looking at the challenges mentioned above, there is an ardent need to make a shift in the way data is collected, stored, assessed, analysed and yields insights. This is where Artificial Intelligence finds opportunity incorrectly leveraging data and run analytics process to gain data insights; impacting business growth.



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AI and machine learning are riding the wave of disruption in life sciences and as it matures it could bring in the following developmental opportunities:

  • Matching patients and clinical trials: Many times it happens that a patient is unwilling to opt for clinical trials or at times unaware if he/she falls in the category to register for a particular clinical trial. Thus, the process becomes intricate and time-consuming. Through AI, a clinical trial can be easily matched with the patient based on the patient health records. This matching or pairing is done by considering data based on location, previous medication history, symptoms, lifestyle or severity of the ailment.

  • Biometric Sensor Feedback: The very first step to better healthcare is knowing that a patient is actively participating in medication and trials. For this, it is essential to record accurate results. But only record of patient observation and manual medical examinations to track symptoms may lead to error. However, with biosensors (AI coupled with IoT) it is easy to record and track heart rate, blood pressure and glucose levels of patients in real time and with utmost accuracy. A system or tool embedded with AI support can recognize anomalies, check abnormal values and provide in-depth information to clinics and patients.

  • Tracing Medical Applications: The traditional method of recording own medication by patients was an obvious problem as the results may be inaccurate. But AI enabled sensors record whenever the medication is administered by a patient. In case the medication is not taken on time the sensors alert the patient. This enables clinics and healthcare companies to identify patients who have followed the prescribed medication. This support in identifying and explaining anomalies and boost accuracy. AI-based sensors through tailored interactions impact behavioral change in patients

  • Understanding EHRs (Electronic Health Records): Despite many healthcare record systems in place, there are many clinics still working with handwritten prescriptions and health records. Such information if not digitally available will be difficult to store and access and may also lead to medication errors. Making a shift to digital platforms like EHRs, it will be easy to store, access and track information and will reduce human errors. Electronically stored data can be easily tracked and processed by intelligent systems for accurate data analysis.

  • Foresee Drug Efficacy: AI systems can easily flag discrepancies, detect anomalies, identify various patterns and can comfortably predict the effectiveness of any drug; based on patient, explicit symptoms and disease. Though doctors can predict based on understanding the symptoms shared by a patient, the scale may be inaccurate. An ai-based system can instantly & accurately predict the efficiency of a drug on a patient by running through history and previous medication details stored in the EHR system.


The Concluding View-


AI in a clinical trial and pharmaceuticals industry is not only limited to the opportunities mentioned above but has the potential to transform any upcoming challenge that can cause trouble in healthcare development. Along with the mentioned challenges, understanding the segments where AI should be actively implemented and what are the limitations associated; need to be known well in advance.

The goal of Artificial Intelligence applications in clinical trials and pharmaceutical industry is to bridge the gap between the information available to patients right now and the information they should know in long term for a healthier life and lifestyle. Adoption of AI technology in clinical trials will streamline the processes and provide precise recommendations. Right from democratizing analytics to improving the state of research, from enhancing human intelligence to empowering decision makers, AI is all about disrupting the clinical trials. Initial advantages of AI are largely associated with the development of drugs but clinical trials too would be benefitted by on-time records, cost effectiveness, reduced administrative efforts and digital processes encouraging patients to be a part of clinical trials.

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